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Löffler K, Scherr T, Mikut R. A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction. PLoS One 2021; 16:e0249257. [PMID: 34492015 PMCID: PMC8423278 DOI: 10.1371/journal.pone.0249257] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 08/03/2021] [Indexed: 11/29/2022] Open
Abstract
Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.
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Affiliation(s)
- Katharina Löffler
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
- Institute of Biological and Chemical Systems - Biological Information Processing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
- * E-mail:
| | - Tim Scherr
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Ralf Mikut
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
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2
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Zhi XH, Meng S, Shen HB. High density cell tracking with accurate centroid detections and active area-based tracklet clustering. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.01.070] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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3
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Wan Y, Hansen C. Uncertainty Footprint: Visualization of Nonuniform Behavior of Iterative Algorithms Applied to 4D Cell Tracking. COMPUTER GRAPHICS FORUM : JOURNAL OF THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS 2017; 36:479-489. [PMID: 29456279 PMCID: PMC5812295 DOI: 10.1111/cgf.13204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Research on microscopy data from developing biological samples usually requires tracking individual cells over time. When cells are three-dimensionally and densely packed in a time-dependent scan of volumes, tracking results can become unreliable and uncertain. Not only are cell segmentation results often inaccurate to start with, but it also lacks a simple method to evaluate the tracking outcome. Previous cell tracking methods have been validated against benchmark data from real scans or artificial data, whose ground truth results are established by manual work or simulation. However, the wide variety of real-world data makes an exhaustive validation impossible. Established cell tracking tools often fail on new data, whose issues are also difficult to diagnose with only manual examinations. Therefore, data-independent tracking evaluation methods are desired for an explosion of microscopy data with increasing scale and resolution. In this paper, we propose the uncertainty footprint, an uncertainty quantification and visualization technique that examines nonuniformity at local convergence for an iterative evaluation process on a spatial domain supported by partially overlapping bases. We demonstrate that the patterns revealed by the uncertainty footprint indicate data processing quality in two algorithms from a typical cell tracking workflow - cell identification and association. A detailed analysis of the patterns further allows us to diagnose issues and design methods for improvements. A 4D cell tracking workflow equipped with the uncertainty footprint is capable of self diagnosis and correction for a higher accuracy than previous methods whose evaluation is limited by manual examinations.
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Affiliation(s)
- Y Wan
- Scientific Computing and Imaging Institute, University of Utah, USA
| | - C Hansen
- Scientific Computing and Imaging Institute, University of Utah, USA
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4
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Thutupalli S, Sun M, Bunyak F, Palaniappan K, Shaevitz JW. Directional reversals enable Myxococcus xanthus cells to produce collective one-dimensional streams during fruiting-body formation. J R Soc Interface 2015; 12:20150049. [PMID: 26246416 PMCID: PMC4535398 DOI: 10.1098/rsif.2015.0049] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 07/09/2015] [Indexed: 01/30/2023] Open
Abstract
The formation of a collectively moving group benefits individuals within a population in a variety of ways. The surface-dwelling bacterium Myxococcus xanthus forms dynamic collective groups both to feed on prey and to aggregate during times of starvation. The latter behaviour, termed fruiting-body formation, involves a complex, coordinated series of density changes that ultimately lead to three-dimensional aggregates comprising hundreds of thousands of cells and spores. How a loose, two-dimensional sheet of motile cells produces a fixed aggregate has remained a mystery as current models of aggregation are either inconsistent with experimental data or ultimately predict unstable structures that do not remain fixed in space. Here, we use high-resolution microscopy and computer vision software to spatio-temporally track the motion of thousands of individuals during the initial stages of fruiting-body formation. We find that cells undergo a phase transition from exploratory flocking, in which unstable cell groups move rapidly and coherently over long distances, to a reversal-mediated localization into one-dimensional growing streams that are inherently stable in space. These observations identify a new phase of active collective behaviour and answer a long-standing open question in Myxococcus development by describing how motile cell groups can remain statistically fixed in a spatial location.
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Affiliation(s)
- Shashi Thutupalli
- Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Mingzhai Sun
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
| | - Filiz Bunyak
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
| | | | - Joshua W Shaevitz
- Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
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Harder N, Batra R, Diessl N, Gogolin S, Eils R, Westermann F, König R, Rohr K. Large-scale tracking and classification for automatic analysis of cell migration and proliferation, and experimental optimization of high-throughput screens of neuroblastoma cells. Cytometry A 2015; 87:524-40. [DOI: 10.1002/cyto.a.22632] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Nathalie Harder
- Department of Bioinformatics and Functional Genomics; Biomedical Computer Vision Group, BioQuant and Institute of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University; 69120 Heidelberg Germany
- Division of Theoretical Bioinformatics; German Cancer Research Center (DKFZ); 69120 Heidelberg Germany
| | - Richa Batra
- Division of Theoretical Bioinformatics; German Cancer Research Center (DKFZ); 69120 Heidelberg Germany
| | - Nicolle Diessl
- Division of Theoretical Bioinformatics; German Cancer Research Center (DKFZ); 69120 Heidelberg Germany
| | - Sina Gogolin
- Division of Neuroblastoma Genomics; German Cancer Research Center (DKFZ); 69120 Heidelberg Germany
| | - Roland Eils
- Department of Bioinformatics and Functional Genomics; Biomedical Computer Vision Group, BioQuant and Institute of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University; 69120 Heidelberg Germany
- Division of Theoretical Bioinformatics; German Cancer Research Center (DKFZ); 69120 Heidelberg Germany
| | - Frank Westermann
- Division of Neuroblastoma Genomics; German Cancer Research Center (DKFZ); 69120 Heidelberg Germany
| | - Rainer König
- Division of Theoretical Bioinformatics; German Cancer Research Center (DKFZ); 69120 Heidelberg Germany
- Integrated Research and Treatment Center, Center for Sepsis Control and Care (CSCC), Jena University Hospital; 07747 Jena Germany
- Network Modeling, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute Jena; 07745 Jena Germany
| | - Karl Rohr
- Department of Bioinformatics and Functional Genomics; Biomedical Computer Vision Group, BioQuant and Institute of Pharmacy and Molecular Biotechnology (IPMB), Heidelberg University; 69120 Heidelberg Germany
- Division of Theoretical Bioinformatics; German Cancer Research Center (DKFZ); 69120 Heidelberg Germany
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Zhong Q, Yang C, Großerüschkamp F, Kallenbach-Thieltges A, Serocka P, Gerwert K, Mosig A. Similarity maps and hierarchical clustering for annotating FT-IR spectral images. BMC Bioinformatics 2013; 14:333. [PMID: 24255945 PMCID: PMC4225570 DOI: 10.1186/1471-2105-14-333] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 11/07/2013] [Indexed: 11/10/2022] Open
Abstract
Background Unsupervised segmentation of multi-spectral images plays an important role in annotating infrared microscopic images and is an essential step in label-free spectral histopathology. In this context, diverse clustering approaches have been utilized and evaluated in order to achieve segmentations of Fourier Transform Infrared (FT-IR) microscopic images that agree with histopathological characterization. Results We introduce so-called interactive similarity maps as an alternative annotation strategy for annotating infrared microscopic images. We demonstrate that segmentations obtained from interactive similarity maps lead to similarly accurate segmentations as segmentations obtained from conventionally used hierarchical clustering approaches. In order to perform this comparison on quantitative grounds, we provide a scheme that allows to identify non-horizontal cuts in dendrograms. This yields a validation scheme for hierarchical clustering approaches commonly used in infrared microscopy. Conclusions We demonstrate that interactive similarity maps may identify more accurate segmentations than hierarchical clustering based approaches, and thus are a viable and due to their interactive nature attractive alternative to hierarchical clustering. Our validation scheme furthermore shows that performance of hierarchical two-means is comparable to the traditionally used Ward’s clustering. As the former is much more efficient in time and memory, our results suggest another less resource demanding alternative for annotating large spectral images.
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Affiliation(s)
- Qiaoyong Zhong
- Department of Biophysics, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum, Germany.
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Cunha A, Tarr PT, Roeder AH, Altinok A, Mjolsness E, Meyerowitz EM. Computational Analysis of Live Cell Images of the Arabidopsis thaliana Plant. Methods Cell Biol 2012; 110:285-323. [DOI: 10.1016/b978-0-12-388403-9.00012-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Rapoport DH, Becker T, Madany Mamlouk A, Schicktanz S, Kruse C. A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters. PLoS One 2011; 6:e27315. [PMID: 22087288 PMCID: PMC3210784 DOI: 10.1371/journal.pone.0027315] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2010] [Accepted: 10/14/2011] [Indexed: 12/13/2022] Open
Abstract
Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters with high reliability and statistical significance. These include the distribution of life/cycle times and cell areas, as well as of the symmetry of cell divisions and motion analyses. The new algorithm thus allows for the quantification and parameterization of cell culture with unprecedented accuracy. To evaluate our validation algorithm, two large reference data sets were manually created. These data sets comprise more than 320,000 unstained adult pancreatic stem cells from rat, including 2592 mitotic events. The reference data sets specify every cell position and shape, and assign each cell to the correct branch of its genealogic tree. We provide these reference data sets for free use by others as a benchmark for the future improvement of automated tracking methods.
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Affiliation(s)
| | - Tim Becker
- Fraunhofer Institution for Marine Biotechnology, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
- * E-mail:
| | - Amir Madany Mamlouk
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
- Graduate School for Computing in Medicine and Life Science, University of Lübeck, Lübeck, Germany
| | | | - Charli Kruse
- Fraunhofer Institution for Marine Biotechnology, Lübeck, Germany
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10
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Dufour A, Thibeaux R, Labruyère E, Guillén N, Olivo-Marin JC. 3-D active meshes: fast discrete deformable models for cell tracking in 3-D time-lapse microscopy. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2011; 20:1925-1937. [PMID: 21193379 DOI: 10.1109/tip.2010.2099125] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Variational deformable models have proven over the past decades a high efficiency for segmentation and tracking in 2-D sequences. Yet, their application to 3-D time-lapse images has been hampered by discretization issues, heavy computational loads and lack of proper user visualization and interaction, limiting their use for routine analysis of large data-sets. We propose here to address these limitations by reformulating the problem entirely in the discrete domain using 3-D active meshes, which express a surface as a discrete triangular mesh, and minimize the energy functional accordingly. By performing computations in the discrete domain, computational costs are drastically reduced, whilst the mesh formalism allows to benefit from real-time 3-D rendering and other GPU-based optimizations. Performance evaluations on both simulated and real biological data sets show that this novel framework outperforms current state-of-the-art methods, constituting a light and fast alternative to traditional variational models for segmentation and tracking applications.
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Affiliation(s)
- Alexandre Dufour
- Institut Pasteur, Quantitative Image Analysis Unit, Paris, France
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11
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Ferry MS, Razinkov IA, Hasty J. Microfluidics for synthetic biology: from design to execution. Methods Enzymol 2011; 497:295-372. [PMID: 21601093 DOI: 10.1016/b978-0-12-385075-1.00014-7] [Citation(s) in RCA: 82] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
With the expanding interest in cellular responses to dynamic environments, microfluidic devices have become important experimental platforms for biological research. Microfluidic "microchemostat" devices enable precise environmental control while capturing high quality, single-cell gene expression data. For studies of population heterogeneity and gene expression noise, these abilities are crucial. Here, we describe the necessary steps for experimental microfluidics using devices created in our lab as examples. First, we discuss the rational design of microchemostats and the tools available to predict their performance. We carefully analyze the critical parts of an example device, focusing on the most important part of any microchemostat: the cell trap. Next, we present a method for generating on-chip dynamic environments using an integrated fluidic junction coupled to linear actuators. Our system relies on the simple modulation of hydrostatic pressure to alter the mixing ratio between two source reservoirs and we detail the software and hardware behind it. To expand the throughput of microchemostat experiments, we describe how to build larger, parallel versions of simpler devices. To analyze the large amounts of data, we discuss methods for automated cell tracking, focusing on the special problems presented by Saccharomyces cerevisiae cells. The manufacturing of microchemostats is described in complete detail: from the photolithographic processing of the wafer to the final bonding of the PDMS chip to glass coverslip. Finally, the procedures for conducting Escherichia coli and S. cerevisiae microchemostat experiments are addressed.
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Affiliation(s)
- M S Ferry
- Department of Bioengineering, University of California, San Diego, California, USA
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12
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Xiao H, Li Y, Du J, Mosig A. Ct3d: tracking microglia motility in 3D using a novel cosegmentation approach. ACTA ACUST UNITED AC 2010; 27:564-71. [PMID: 21186244 PMCID: PMC3035800 DOI: 10.1093/bioinformatics/btq691] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION Cell tracking is an important method to quantitatively analyze time-lapse microscopy data. While numerous methods and tools exist for tracking cells in 2D time-lapse images, only few and very application-specific tracking tools are available for 3D time-lapse images, which is of high relevance in immunoimaging, in particular for studying the motility of microglia in vivo. RESULTS We introduce a novel algorithm for tracking cells in 3D time-lapse microscopy data, based on computing cosegmentations between component trees representing individual time frames using the so-called tree-assignments. For the first time, our method allows to track microglia in three dimensional confocal time-lapse microscopy images. We also evaluate our method on synthetically generated data, demonstrating that our algorithm is robust even in the presence of different types of inhomogeneous background noise. AVAILABILITY Our algorithm is implemented in the ct3d package, which is available under http://www.picb.ac.cn/patterns/Software/ct3d; supplementary videos are available from http://www.picb.ac.cn/patterns/Supplements/ct3d.
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Affiliation(s)
- Hang Xiao
- Department of Biophysics, Institute of Neuroscience, Shanghai Institutes for Biological Sciences, 200031 Shanghai, China
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Nath SK, Palaniappan K. Fast Graph Partitioning Active Contours for Image Segmentation Using Histograms. EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING 2010; 2009:820986. [PMID: 35599853 PMCID: PMC9121993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We present a method to improve the accuracy and speed, as well as significantly reduce the memory requirements, for the recently proposed Graph Partitioning Active Contours (GPACs) algorithm for image segmentation in the work of Sumengen and Manjunath (2006). Instead of computing an approximate but still expensive dissimilarity matrix of quadratic size, ( N s 2 M s 2 ) ∕ ( n s m s ) , for a 2D image of size Ns ×Ms and regular image tiles of size ns ×ms , we use fixed length histograms and an intensity-based symmetric-centrosymmetric extensor matrix to jointly compute terms associated with the complete NsMs × NsMs dissimilarity matrix. This computationally efficient reformulation of GPAC using a very small memory footprint offers two distinct advantages over the original implementation. It speeds up convergence of the evolving active contour and seamlessly extends performance of GPAC to multidimensional images.
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Affiliation(s)
- Sumit K Nath
- Department of Computer Science, University of Missouri, Columbia, MO 65211, USA
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Jaeger S, Song Q, Chen SS. DYNAMIK: a software environment for cell DYNAmics, Motility, and Information tracKing, with an application to Ras pathways. ACTA ACUST UNITED AC 2009; 25:2383-8. [PMID: 19578170 DOI: 10.1093/bioinformatics/btp405] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
The emergence of new microscopy techniques in combination with the increasing resource of bioimaging data has given fresh impetus to utilizing image processing methods for studying biological processes. Cell tracking studies in particular, which are important for a wide range of biological processes such as embryonic development or the immune system, have recently become the focus of attention. These studies typically produce large volumes of data that are hard to investigate manually and therefore call for an automated approach. Due to the large variety of biological cells and the inhomogeneity of applications, however, there exists no widely accepted method or system for cell tracking until today. In this article, we present our publicly available DYNAMIK software environment that allows users to compute a suit of cell features and plot the trajectory of multiple cells over a sequence of frames. Using chemotaxis and Ras pathways as an example, we show how users can employ our software to compute statistics about cell motility and other cell information, and how to evaluate their test series based on the data computed. We see that DYNAMIK's segmentation and tracking compares favorably with the output produced by other software packages.
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Affiliation(s)
- Stefan Jaeger
- Partner Institute of Computational Biology, Shanghai Institutes of Biological Sciences, Chinese Academy of Sciences, Max-Planck Society, Shanghai 200031, China.
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